title: An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction.
abstract: We present a formalism to compute the probability of an amino acid sequence conformation being native-like, given a set of pairwise atom-atom distances. The formalism is used to derive three discriminatory functions with different types of representations for the atom-atom contacts observed in a database of protein structures. These functions include two virtual atom representations and one all-heavy atom representation. When applied to six different decoy sets containing a range of correct and incorrect conformations of amino acid sequences, the all-atom distance-dependent discriminatory function is able to identify correct from incorrect more often than the discriminatory functions using approximate representations. We illustrate the importance of using a detailed atomic description for obtaining the most accurate discrimination, and the necessity for testing discriminatory functions against a wide variety of decoys. The discriminatory function is also shown to be capable of capturing the fine details of atom-atom preferences. These results suggest that the all-atom distance-dependent discriminatory function will be useful for protein structure prediction and model refinement.
authors.affiliation: Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, 9600, Gudelsky Drive, Rockville, MD 20850, USA.
authors.forename: R
authors.initials: R
authors.lastname: Samudrala
authors.forename: J
authors.initials: J
authors.lastname: Moult
mesh_terms: D008962:Models, Theoretical; D011336:Probability; D011487:Protein Conformation; D055672:Static Electricity